Open data genomics_palermo_2017_ver03

Open Data is Essential for
Personalized Medicine
BF Francis Ouellette
https://goo.gl/8U1QJa

Open Data is Essential for Genomics
This presentation is on:
https://www.slideshare.net/

@bffo
francis@genomequebec.comE-mail

Times I’ve been in Italy
• Trieste 1996: Last Yeast Genome Meeting
• Naples 2005: NETTAB “Workflows management:
new abilities for the biological information overflow”
• Rome 2017: Elixir
• Palermo 2017: NETTAB

Outline
• What I do
• Open Data in genomics
• Final thoughts

But first, a little about me …
… an unfinished story!

https://goo.gl/anu933

http://goo.gl/dJIur

http://goo.gl/LwVOZ

http://goo.gl/QI6aL

http://goo.gl/mYHFO

http://goo.gl/Jc5TK

https://goo.gl/3PFr7L
1993-1997

from the National Centre for Biotechnology Information

from the National Centre for Biotechnology Information
PANIC

PANIC

https://www.ubc.ca/

1999

2001: Human Genome Project

2003-2007

Toronto

2007-2017

International Cancer Genome Consortium

2017- …

SABs, EBs & projects I’m on:

So what unifies all
of what I’ve done?

So what unifies all
of what I’ve done?
Helping scientists do science.

Open Data
https://goo.gl/Z63Wxp

Genomics
https://goo.gl/MX84KA

What am I calling “Genomics”?
All “omics”
– DNA and RNA, +Epigenomics
– Proteomics, +Protein Interactions, +Pathways
– Metabolomics
– Bioinformatics/Computational Biology
– All of the related data and metadata
• Phenotype
• Clinical
• Images
– New technologies …

Biological scope?
• Anything with DNA or RNA or protein

Example of one of a
challenge for all of us?
The integration of genomic data
with deep learning and artificial
intelligence

AI, Big Data, Deep Computing
• Artificial Intelligence / Deep Learning and
the Big Data Hype?
https://goo.gl/WHg36Q

What do we need for that?
https://goo.gl/JWpXj2

What else?
• Data has to be FAIR
– TO BE FINDABLE
– TO BE ACCESSIBLE
– TO BE INTEROPERABLE
– TO BE RE-USABLE
• https://www.force11.org/group/fairgroup/fairprinciples

Big data examples
• Genomic sequences
• Imaging
• Population scale collected wearable data

Data Center for all in Québec?
• Health Care in Canada is governed
province by province.
• Génome Québec is working with various
ministries to set something that could be
useful/centralized and make genomic data
usable for research (controlled access).
• Needs to include clinical data

“Building a data centre is
like making pancakes, you
always need to throw
away the 1st one”
Robert Grossman
Frederick H. Rawson Professor and
the Director of the Center for Data
Intensive Science (CDIS) at the
University of Chicago
http://rgrossman.com/

Sharing all data types,
including clinical data?
https://goo.gl/ofEPeX

Authors present at the
“Toronto meeting”
https://goo.gl/ofEPeX

53 Introduction 1.0
Open data critical to
progress in Science

54 Introduction 1.0
One example: GenBank
GenBank sequence
database is an open
access, annotated
collection of all publicly
available nucleotide
sequences and their
protein translations.

55 Introduction 1.0
Open data critical to progress in Science
• Without GenBank and other public
sequence databases
– There would be no BLAST
– There would be no diagnostics DNA testing
– There would be no understanding of the
human genome (there probably would not
have been a human genome to work on in the
first place).

Adapted from Niko Beerenwinkel ,Chris D. Greenman ,Jens Lagergren
ICGC PCAWG
Docker
Testing
Computational Cancer Biology: An Evolutionary Perspective
•Published: February 4, 2016. https://doi.org/10.1371/journal.pcbi.1004717

Cancer is a Disease
of the Genome
Challenge in Treating Cancer:
 Every tumour is different
 Every cancer patient is different
Adapted from Tom Hudsonhttps://www.cancer.gov/research/areas/genomics

Analysis Data Types
• Simple Somatic Mutations (SSM or SNV)
• Copy Number Alterations (CAN or CNV)
• Structural Variants (SV)
• Germline variants (SNPs)
• Gene Expression (micro-arrays and RNASeq)
• miRNA Expression (RNASeq)
• Epigenomics (Arrays and Methylation)
• Splicing Variation (RNASeq)
• Protein Expression (Arrays)

International Cancer Genome Consortium
• Collect ~500 tumour/normal pairs from each of 50 different major
cancer types; 25,000 T/N pairs!
• Comprehensive genome analysis of each T/N pair:
– Genome
– Transcriptome
– Methylome
– Clinical data
• Make the data available to the research community & public.
Identify
genome
changes
…GATTATTCCAGGTAT… …GATTATTGCAGGTAT… …GATTATTGCAGGTAT…
Adapted from Tom Hudson

ONTARIO INSTITUTE FOR CANCER RESEARC
60

International Cancer Genome Consortium: http:/icgc.org

ICGC needs to deal with different
kinds of users!
62
• Biologists/Clinicians:
– Web interface to processed data, providing:
• Affected gene lists with consequences
• Impact on pathways
• Power users:
– Application Programing Interface (API) to get
to data
– Availability and Integration with cloud
resources

ICGC Data Coordinating Centre:
dcc.icgc.org
63

https://dcc.icgc.org/
64

65
https://dcc.icgc.org/icgc-in-the-cloud

66
http://www.cancercollaboratory.org/

Some challenges:
67
• So, we have lots of data, is
it generated the same way?

Every country/group has basically
been submitting:
68
– Simple Somatic Mutations (SSM or SNV)
– Copy Number Alterations (CAN or CNV)
– Structural Variants (SV)
– Germline variants (SNPs)
– Gene Expression (micro-arrays and RNASeq)
– miRNA Expression (RNASeq)
– Epigenomics (Arrays and Methylation)
– Splicing Variation (RNASeq)
– Protein Expression (Arrays)

Are they all using the same
pipelines?
69
• No

70

Steering Committee of PCAWG
71
• Peter Campbell, Sanger Inst.
• Gady Getz, Broad
• Jan Korbel, EMBL
• Lincoln Stein, OICR
• Josh Stuart, UCSC

PanCancer Analysis of Whole
Genomes (PCAWG)
• > 2,800 T/N pairs with clinical data from 20
tumour type of whole genome analysis.
• Aligned with one standard pipeline.
• Genomic Variants determined with 3 pipelines
• 17 working groups
• > 50 Papers are being
written now.

https://www.biorxiv.org/search/pcawg

Deliverable for PCAWG include:
74
• 1st PANCANCER analysis on > 2,800
cancer tumours from a WGS perspective
• RNA, SSM, CNV, Methylation analysis &
germline
• Published (executable) pipelines
– Docker / Dockstore
– Mutiple cloud access to data
– Multiple portal access to data

https://dcc.icgc.org/pcawg
75

Working Groups (1/2)
76
1. Novel somatic mutation calling methods
2. Analysis of mutations in regulatory regions
3. Integration of transcriptome and genome
4. Integration of epigenome and genome
5. Consequences of somatic mutations on pathway
and network activity
6. Patterns of structural variations, signatures,
genomic correlations, retrotransposons, mobile
elements
7. Mutation signatures and processes
8. Germline cancer genome

Working Groups (2/2)
77
9 Inferring driver mutations and identifying cancer
genes and pathways
10 Translating cancer genomes to the clinic
11 Evolution and heterogeneity
12 Exploratory: portals, visualization and software
infrastructure
13 Molecular subtypes and classification
14 Analysis of mutations in non-coding RNA
15 Exploratory: mitochondrial
16 Exploratory: pathogens
17 Tech Technical working group

https://goo.gl/AMxwSU

http://dockstore.org
82

Docker Testing Group
• Group that to ensure all container
workflow work as expected.
https://goo.gl/AMxwSU

Access to Data?
• Human Data
• Patients consented to have their DNA
looked at so people could understand
cancer
• Need to have a system to maximize
people’s gift to science.

Identify
yourself
Fill out detail form which
includes:
• Contact and Project
Information
•Information Technology
details and procedures
for keeping data secure
•Data Access Agreement
All of these
documents are
put into a PDF
file that you
print and get your
institution to sign
off on your behalf

89
https://icgc.org/daco/approved-projects
314 groups

DACO
ICGC
dbGaP
GDC
EGA
TCGA
BAM
Open
Open
ERA
BA
M
BA
M
EGA id
& password
WGS
Ger m
Line

Challenge:
• Open Data and controlled access data
• Not enough eyeballs on the data
• Eyeballs on the data needed to make
discoveries.
https://goo.gl/ogbWXG

Culture of Sharing Openly
• Public Funding agencies
• Consortiums
• Mentors
• Peers
• New generation (vs my old generation)
• Has to become the norm

Final thoughts …
• Access to data is essential for science
• Getting data that is FAIR is hard work
• It is essential to share the work you do if
you want to be recognized, get tenure, get
a job or a promotion.
• Human data is more complicated, but
don’t let that get in the way!
• There is a lot of material out there, learn
from it (& cite your sources)!

Last message to students and
young PDFs and investigators:

Last message to students and
young PDFs and investigators:
Be open so people
can see how great
you are!

ONTARIO INSTITUTE FOR CANCER RESEARC
96
915

DCC Software
Developer
Vincent Ferretti
Dusan Andric
Phuong-My Do
Francois Gerthoffert
Terry Lin
Michael Moncada
Vitalii Slobodianyk
Bob Tiernay
Douglas Wong
Linda Xiang
Junjun Zhang
Acknowledgments
ICGC/OICR
Project leaders:
Tom Hudson
John McPherson
Lincoln Stein
Jared Simpson
Paul Boutros
Vincent Ferretti
Francis Ouellette
Jennifer Jennings
Ouellette Lab
Alysha Moncrieffe
Ann Meyer
Zhibin Lu
Web Dev
Joseph Yamada
Kaman Wu
Kim Cullion
Koji Miyauchi
Miyuki Fukuma
ICGC DCC Biocuration
Hardeep Nahal
Marc Perry
http://oicr.on.ca http://icgc.org
… and all the patients and their
families that that are putting
their hopes into our work!
Research
IT/Systems
David Sutton,
Bob Gibson
David Magda
Rob Naccarato
Brian Ott
Gino Yearwood
EGA
Jordi Rambla De
Argila
Arcadi Navarro
Audald Iloret
Mauricio Moldes

|
ÉQUIPE DES AFFAIRES SCIENTIFIQUES
9827 mars 2017
B.F. Francis
Ouellette
Annina Spilker
Joël Savard
Diana IglesiasDiane
Bouchard
Cristina CiurliMicheline
Ayoub
Hélène
Fournier

99
Grazie

100

Open data genomics_palermo_2017_ver03

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